Embodiments of the present invention relate generally to decision support tools, and more particularly relate to a decision framework for selecting graph types.
Graphs, such as line graphs, bar graphs, pie charts, and the like, are effective tools for visualizing and understanding large quantities of data. However, choosing an appropriate graph type for a given situation can be a complex task. The selection of a graph type depends on many factors, including the types of data to be plotted and the kinds of tasks to be performed by end-users. Furthermore, there are hundreds of different graph types, each having a different effect on the types of insight that may be gained from the underlying data.
Prior publications by researchers in the field of information visualization expose the complexities of graph selection, but do not provide a step-by-step methodology for determining the most appropriate graph type for a particular scenario. Many researchers take a partial approach to helping users identify appropriate graph types by organizing graphs. A similar approach is taken by popular spreadsheet programs (e.g., Microsoft Excel) and other data analysis systems (e.g., SPSS). These attempts to organize graphs cluster them together according to various functional categorizations (e.g., data, form, geometry). For example, the “chart wizard” in Excel presents users with graph types organized by form (e.g., bar, pie, line, area, etc.), thereby allowing users to select a graph type based on a desired visual representation of the graph.
A problem with the above approaches is that each categorization restricts the manner in which users may locate appropriate graphs. For example, a data categorization may be useful for analysts with a clear understanding of the form of the data they need to display. A data categorization may be less useful for designers who are selecting a graph type from an end-user-based perspective.
A related problem is that most existing graphing tools such as Excel categorize graph types according to form-based characteristics. Current tools do not adequately account for the intended end-users of a graph, or the types of insight that those end-users are expected to gain. As a result, these tools may recommend graph types that are ultimately inappropriate for a given target audience or task.